Shortest Job First Load Balancing Algorithm for

Shortest Job First Load Balancing Algorithm for Efficient Resource Management in Cloud Moomina Waheed, Nadeem Javaid∗ , Aisha Fatima, Tooba Nazar, Komal Tehreem, Kainat Ansar ∗

COMSATS University, Islamabad 44000, Pakistan Correspondence: www.njavaid.com, [email protected]

Abstract. Energy is among the most valuable resource in the world that need to be consumed in an optimized manner. For making intelligent decisions in energy consumption Smart Grid (SG) is introduced. One of the key components of SG is communication. Cloud-Fog based environment is the most popular communication architecture nowadays. Keeping the focus on this point this article proposed an integration of Cloud-Fog based environment with Micro Grid (MG) for effective resource management. For experimentation, the word is divided into 6 regions based on the division of continents. Each region contains 6 clusters and 3 fogs connected to each of them with MG and centralized cloud. Cloud Analyst simulator is used for testing of our proposed scenario. To cater the huge load on fogs a new load balancing technique Shortest Load First(SLF) is introduced in the simulator. The load balancer technique is used to manage the requests on fogs whereas the dynamic service proximity policy is used for connection of clusters with fogs. Key words: Microgrid, Cloud Computing, Fog Computing, Load Balancing, Shortest Job First, Dynamic Service Proximity, Cloud Analyst.

1 Introduction Effective Energy consumption is one of the most debatable topics of modern world issues. With ongoing research in this field, SG seems to be a promising solution for the problem. The integration of Information Communication Technology (ICT) with SG makes it more reliable for effective energy management. SG includes a variety of operational devices such as Smart Meters, MG, and Smart Appliances and Renewable energy resources. In order to maintain the load during peak hours, and shift load from on-peak hours to off-peak hours SG seems to be the best platform to work with. A key ingredient of SG is the effective communication between the consumer and utility. Research is ongoing in this paradigm to introduce more effective communication tool. Cloud computing is a network of remote servers, that provides sharing of resources such as storage, processing, and computation. For this purpose, cloud needs an effective environment for communication with low latency and delays. Computer Information System Company (Cisco) provides an effective solution

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Moomina Waheed et al.

for it in form of fog computing. Fog computing extends cloud computing to the edge networks.It acts as a middle layer between cloud and consumer thus removing the distance between them. Fog minimize the response time and also reduce the burden on the cloud.[1] However, it stores the data on a temporary basis so cloud is required for storing the data permanently. In addition to this in case of fogs unavailability, the consumer can access through the cloud. Keeping in view the importance of communication, an integration of cloud and fog based environment is introduced in energy management for effective resource sharing. In this integration, consumer communicates with the fog for their energy requirement. As a result, fog communicates with MG and cloud to fulfill their demand. This integration is made as possible as there is high demand from users. However, this integration suffers from certain limitation such as high latency, low response rate, data loss, security and load balancing. Among all these issues, load balancing is a key issue as it affects the processing time as well as the response time of the cloud. In order to overcome these issues certain load balancing algorithm such as Round Robin, Throttle is used in Cloud Analyst; a simulator used for making simulation in the cloud environment. This paper is the extended version of [1]. Shortest job first Load Balancing technique is introduced in this paper for load balancing in Cloud Analyst Environment 1.1 Motivation The paper [1], [2] made a significant contribution by the collation of cloud fog based infrastructure with SG. They have used Cloud Analyst simulator for testing of their proposed scenario. Cloud analyst simulator is an effective tool, as it contains service broker policies for finding the appropriate fog and load balancing technique for managing the load on Virtual Machines (VMs). The load balancing techniques used in cloud analyst are Round Robin, throttle and active load balancing. However, the authors in [4] claims that each of them has a certain limitation. Round Robin assign a task without checking the capacity of the server. Throttle does not consider processing time of each individual request. Active load balancing assigns the VM with minimum load but does not check whether it is previously utilized or not as claimed in [3]. The cloud analyst simulator motivates us to do research in this area. With an essence of exploring in Cloud analyst Shortest Job First load balancing (SJF) technique is used. The SJF combined with advanced dynamic service broker policy for getting the appropriate results. 1.2 Contributions The contributions of this paper are summarized as follows – Cloud and Fog based environment is integrated with the MG for effective resource sharing.

Fog and Cloud based Environment for Resource Distribution in SG

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– As fogs suffer from the issue of load balancing so as to overcome this problem SLF algorithm is introduced in the cloud analyst simulator. – The SLF is responsible for increasing the processing time as the shortest load are executed first. Thus, reducing the latency and increasing the response time.

2 Literature Review From the past couple of years, researchers are working for the integration of cloud in SG. The purpose of integration is effective communication and resource sharing. Mostly, they focused on using cloud infrastructure as a communication model. There is a rich scientific literature available on cloud computing used for effective resource sharing. Multiple simulation tools like GridSim, CloudSim, and Cloud Analyst are developed for the testing purposes. However, the scope of these contribution are limited to effective resources sharing. Djabiret al.[4], proposed a scheduling technique for solving the problem of charging and discharging of Electric Vehicles (EVs) at the public supply station. Their contributions are based upon formulating the problem of charging and discharging of EVs. They have introduced a new communication architecture between SG and cloud platform through the integration of SG (Smart Grid), cloud, EVs and Electric Vehicles Public Supply Station (EVPSS). Furthermore, they have presented a priority scheduling algorithm to maintain the demandsupply curve. However, the paper lacks any contribution regarding latency and security issue of Cloud. Zijianet al.[5], address cost optimization problem of demand-side computing. They have proposed a cost-oriented model that allocates the resources by considering the load profile of consumer from Cloud. Modified Priority List (MPL) algorithm is used to solve the complex computation and different case scenarios are discussed by varying the peak load. Nonetheless, in case of data redundancy or loss, the performance of the algorithm is affected. In [6], authors presents a survey of the existing load balancing policies using cloud analyst. The pros and cons of each policy are discussed along with a solution to overcome the existing problem of each policy. In[7], Rabindra et al.focus upon using fog computing as a communication model for MG. Thus, reducing the communication complexity by making intelligent decisions. The results of fog and cloud computing are compared with each other. Fog severs as a middle layer for minimizing the response time. However, in case of Fog failure, there are chances of data lost. Mohammad Abdullahet al.[8], considers the problem of effective energy management. They have proposed a model for providing energy management as a service over Fog. The implementation results in flexibility, interoperability, connectivity and provides data required for Fog energy management. However, the experiments are limited to MG experiments which are needed to be done at large scale for making it an effective service over Fog.

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Neeraj et al.[9], focus on making a system that is self-sustained to fulfill its energy requirements. For this purpose, they have used a multi-tenant cloud based Nano grid that is self-sustainable in order to fulfill its requirements. The energy is generated through Photovoltaic (PV) panels and wind to fulfill the demand. In the worst case, energy is taken from the grid. However, the energy generation is limited to PV and wind, if other renewable resources such as fuel cell, hydropower are involved then more energy is generated. In [1], author propose a model for effective resource sharing through the integration of cloud and fog based environment. For this purpose, they have introduced a new dynamic service proximity service broker policy in cloud analyst. The policy focuses upon selecting the fog with minimum latency and manage the traffic by using VMs. The results of the policy are compared with Round Robin load and Throttle. In overall performance Throttle outperforms RR. Nonetheless, Throttle does not consider the advanced load balancing requirement such as processing time of each request. The authors in [10], solved the problem of resource allocation by using ant colony system. The performance parameter are: the processing time (PT), energy consumption and cost with RR algorithm. Mainly two objectives are resolve in this paper: balancing load and minimization of energy consumption. A novel service broker policy is introduced for Fog selection in [11]. The authors propose a machine learning technique; fuzzy logic and fuzzy rules for selecting the Fog. The paper shows a good response time. The author in [12], proposed a technique based on Certificate Less Provable Data Possession (CL-PDP) method. The method is proposed for the aggregation of cloud with SG. The primary focus of the paper is to provide sufficient storage, processing capacity and security using data management system schemes. Furthermore, in [13],[15]the authors use the Recurrent Neural Network (RNN) for energy management. The RNN is highly proficient as the data increase. In order to clearly search the problems of this domain, an extensive literature studies have been done. The aim was to determine the problems of the cloudfog environment and try to overcome them. Mainly, we found out that cloud computing is facing challenges in the area of security and resource allocation. It also faces some latency issues due to the huge geographical distance of clouds. 2.1 Problem Formulation We have considered the problem of load balancing in fog-cloud based infrastructure. We define load as a task that is executed whenever a cluster sends a request to fog then a set of n load is submitted to multiple fogs. These loads can be pre sented as load = L1 , L2 , ..., Ln Each load execution is considered as a task. Thus there is a set of r task. Each fog contains a set of m VM represented as V M = vm1 , vm2 , ..., vm . The tasks are distributed among VM for execution n such as LoadT asks = LoadT asksa1 , LoadT asksb2 , ..., LoadT asksm n . First task (LoadT asks21 ) is executed on Fog1 by the VM 2 ,Second Task (LoadT asks42 ) is executed on Fog2 by the VM 4 and so on. Each Fog VM denoted asF V Mj execute as set of decomposed loads set or we can say task. So,


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Table 1. Related Work Technique (s) Objective (s) Features PSA [4] Charging and discharging of EVs through EVPSS MPL [5] Cost optimization problem of demand side computing Comparison Comparisons of poliof Load cies Balancing Techniques [6] Fog Comput- Fog Computing as ing [7] a communication model for MG MG, Fog [8] Energy management as a Service over Fog Multi-tenant Self-sustainable sys[9] tem to fulfill its requirements Cloud Ana- Effective resource lyst [10] management

(s)

Limitation (s)

Energy Consumption No latency issue is covManagement , ered Cost reduction cloud

using Data redundancy effects performance

Response Time and Pro- Limited to few policies cessing Time Comparisons

Intelligent decisions making, reduction in complexity Flexibility, interoperability, connectivity Energy Production through Renewable Sources Service broker policies are used to reduce latency ML [11] Effective fog selec- Prediction are made for tion energy management MORAACS Load balancing and Resource selection by [12] reduce energy con- following patters sumption CL-PDP [13] Aggregation of cloud Sufficient storage, prowith SG for Sufficient cessing capacity and sestorage and Process- curity ing RNN [14] Energy management Energy management through predictions

Fog failure results in data loss Limited to MG Reliable energy resources are required Load need to balance along with latency reduction Minimum response time can be achieved Response Time needed to be reduced Huge Data is required to train the system

Huge data is required for training

for assigning the load to F V Mj for task execution we can state it as follow F V Mj T asks = LoadT asks1 aj , LoadT asks2 bj , ..., LoadT asksn mj . By taking the union we can get a complete set of all task executed by the multiple VMs on multiple fogs. For example (LoadT asks1 24 ) 1s t task is carried out by the 2n d VM of 4t h fog. So processing time can be calculated by the formula j j P rocessingT ime(F V Mj T asks) = LoadT asksi kStartT ime +LoadT asksi kExeT ime (1) j 0 ≤ β ≤ 1. Where i belongs to set of selected task LoadT asksi kStartT i ime is j the starting time of task loadT asksi kExeT is the process execution time The ime

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response time can be calculated using the formula ResponseT ime(F V Mj T asks) = ResponseT imetotal + N etworkdelay ResponseT imetotal = ResponseT imef inish − RrsponseT imearrival N etworkdelay =

BW × Trate Sizeof dataunit

(2) (3) (4)

The cost can be calculated by adding the processing time and response time j X j Cost(F V Mj T asks) = M in[ = 1(CostF unction(LoadT askik , F V Mj ))] (5) m j Where CostF unction(LoadT askik , F V Mj ) = w1 P rocessingT ime(F V Mj T asks)+ w2 2ResponseT ime(F V Mj T asks) where, w1 andw2 represents fixed weights. The weights are use to emphasize the importance of each objective function

3 System Model In this paper, cloud and fog based integrated environment is used with SG. Cloud computing uses a network of servers from a remote location for storage, processing, and computing of resources. The fog computing act as a middle layer between the cloud and end user. It first receives the request from the user and then fulfills its demand. The fog computing is used with the cloud to reduce the load of cloud and for removing the latency due to large geographical distance. A centralized cloud is required for managing the huge data as well as it is required to make the decision in case of fog failure or shifted to another location. The following figure demonstrates our proposed model. In the proposed model, the world is divided into 6 regions based upon the geographical distribution of the world. Furthermore, a 3 tier architecture is used for allocation of resources. The first tier contains a centralized cloud. The fogs from all 6 regions are directly connected to the centralized cloud for communication and management. The second tier contains fogs to minimize the response time for the request. Each region is assigned 3 fogs having 50 VMs each for equal distribution of load . The 3 tier contains buildings that are grouped together in clusters. Each cluster contains 100 buildings having 10 to 13 apartment in each. In nutshell depending upon the regions, we have 12 clusters. The cluster communicates with the fog for their demand. The fog communicates with MG and then MG transmits the supply to the fulfill the demand. The fog is further connected with a cloud in case if a fog is unable to fulfill the demand its requests are sent to the cloud to find the nearby MG and fulfill the cluster demand.


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Cloud Service Provider

Macro Grid Saas

Paas

Iaas

Tier 1 R1

Fogs

Rn LB

LB

VM

VM

Fog1 LB

Fog 16 LB

LB

Fog 2

LB VM

VM

VM

VM

Fog 17

Fog 3

Fog 18

Tier 2

MG

MG

MG

MG

Controller

Controller

Controller

Controller

C1

Cn

C1

Cn

Tier 3

End Users Legend: Energy ow Two way communicaon ow One way communicaon ow

Fig. 1. Cloud-Fog integration for efficient energy allocation

3.1 Load Balancing Algorithms The purpose of using the load balancing algorithm is to manage the workload of fogs. The load is managed by minimizing the network delay. In our scenario the network delay occurs between the requests generated from the cluster towards the fog. A new load balancing technique based on the SLF scheduling algorithm is proposed. The main purpose of the proposed scheme is to first serve those request that has the minimum request time. So, first we create a list and sort the upcoming request based on the request length. Then assign the request to the VMs for further processing

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3.2 Shortest Load first Algorithm Shortest Load first is a scheduling algorithm that is no preemptive in nature means that once a VM is executing a request other request cannot be allocated to the VM. The algorithm maintains the load by checking the length of the request. The request with the smallest size is executed first. So, SLF gives priority to request with smallest size. In the proposed scenario, the request are called as load. Thus executing the shortest load first. This mechanism helps to achieve high efficiency and low turnaround time. 3.3 Algorithm The basic SLF algorithm is modified according to its implementation in cloud analyst simulator. – First in User Bases Class, we use a random function with request length to generate the request with multiple lengths. – In Datacenter Broker create a list to receive the request. – Sort the receive list in ascending order. – VmLoadBalancer maintains the Load list by checking the size of the request. – VmLoadBalancer selects the load with shortest request time and executes it first. – If any two loads have the same request time then First Come First server condition is used to execute the load. – Until the execution of a load all other loads waits in a queue, once the execution of a load is completed the VmLoadBalancer moves towards next load. – Calculate the response time. – Stop the process once all load requests are executed. 3.4 Service Broker Policies Service Broker Policies are mainly used to search the fog for the cluster to send its request to it. Mainly two service broker policies are discussed in this paper. Service Proximity Policy and Optimize Response time. The below section discuss each policy in detail. 3.5 Service Proximity One of the simplest yet most effect service broker policy is the Service proximity policy also known as a closet data center. The policy works on the principle of finding a path that has the minimum latency among the fog and cluster. The fog with minimum distance is selected and assigned to the cluster. 3.6 Optimize Response time A table is maintained that contains the best Response time of all the fogs. Whenever a cluster sends a service request than the fog with best response time is selected and assigned to the cluster.


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4 Simulation Results and Discussion The following section is used to discuss the results and simulations of our proposed scenario. 4.1 Simulation Setup The simulations are done by using cloud analyst simulator. A core-i7 system is used with 8GB RAM and IT HDD for getting fast results. 4.2 Discussion Cloud analyst is popular tool due to its flexibility and reliable interaction. It is very helpful in comparing different results. It contains service broker policies such as service proximity policy, optimize response time and dynamic reconfigure policy for selecting the fogs. Load balancing techniques such as RR, throttle, and active load balancing are used. However, each of them has certain limitations that are discussed in the above section. The paper propose SLF load balancing technique and compares its results by using different service broker policies. The world is divided into 6 region based on the division of its continents. One continent is omitted due to the lack of energy consumption their. Each region contains 3 fogs and 2 clusters having 100 buildings in them. Each building has 10 apartments in it. In nutshell, we have 18 fogs and 12 clusters to fulfill the requirements of 6 regions of world. The above mentioned scenario predicts a huge amount of heterogeneous data. The fogs are connected to a centralized cloud for permanent storage of such huge data. Each Fog contains VMs in order to avoid the cost of a physical machine. The requests from each apartment is sent to Fog. The fog receive the request from the apartments and communicates with MG to fulfill the demand. The MG fulfill the desired demand. We have done simulations for different load balancing policy and compare their results. The inputs are same for each load balancing technique. Table 2. Regions of World Region Oceania Africa Asia Europe South America North America

Region Id 5 4 3 2 1 0

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4.3 Shortest Load First In shortest load balancer, the vmloadbalancer firstly maintains the loads in a list by checking their size and arranges them in ascending order. So, the shortest load will executed first. After maintaining the list, the vmloadbalancer checks for the VM with minimum allocations or lightest load. It assigns the load to the VM with minimum allocations. In case 2, if the loads have the same length then the FCFS is used to execute the load. The average, minimum and maximum response time of clusters are given below in the following Table 3. Table 3. Response Time Cluster

Avg (ms)

Min (ms)

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12

62.54 61.91 72.52 72.01 68.51 68.14 58.37 57.85 70.69 72.36 62.06 66.25

38.07 40.25 39.52 37.66 40.75 39.90 38.83 39.64 45.12 49.25 50.89 42.29

Max (ms) (%) 184.37 185.37 263.56 263.35 259.80 256.59 130.73 132.97 261.59 260.08 187.99 186.31

Processing Time (PT) is not much high as three fogs are used in each region. The cluster having the minimum distance from the fog has the minimum PT. The goal of using SLF was to reduce the processing time as it first fulfills the request with shortest load. The Table 4 shows a comparison of SLF results with two different service broker policies. The service proximity policy indicates that SLF best respose time where as optimize response time policy result is slightly a bit higher than service proximity policy. The comparison is made on the basis of response time and process time . Table 4. Over All Response Time Summary Service Broker policy Closest Data Center Proximity Policy

Average (ms) 52.66

Min (ms)

Max (ms)

37.66

263.56

66.43

42.89

289.66


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The Fig 2 shows expected cost of SLF with service proximity policy.The total cost is computed on the basis of three parameter such as VM cost, data transfer cost and micro grid cost.

800 700 600 VM Cost($) Microgrid Cost($) Data Transfer Cost($) TotalCost($)

Cost($)

500 400 300 200 100

0 Fog1 Fog3 Fog2 Fog5 Fog4 Fog7 Fog6 Fog8 Fog9 Fog10 Fog11

Fogs

Fig. 2. Cost

4.4 Round Robin Round Robin divides the time slices among all the available VMs. Each VM gets an equal time interval for the execution of a task. Once the time is over, the next coming request is assigned to the next VM in such a way that VM got an equal chance to work and no VM sits idly. The advantage of using RR is that it provides equal execution opportunity to all the request. Thus, the RR response time beats another algorithm. However, we have noticed a problem as scalability of RR is increased its performance starting deteriorating.. Processing Time (PT) is maintained to its minimum by giving equal time to each VM. In this way, no VM gets over burden.The The Fig 3 shows the total cost is calculated by adding the VM cost, data transfer cost, and microgrid cost. The Table 6 shows a comparison of RR results with two different service broker policies ie Service proximity policy and optimize response time. The comparison is made on the basis of RT and process PT.

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Moomina Waheed et al. Table 5. Response Time Cluster

Avg (ms)

Min (ms)

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12

61.79 61.89 72.50 72.22 68.29 67.87 58.85 57.96 70.69 70.36 69.06 67.25

39.12 40.35 40.07 37.28 41.41 40.36 38.92 40.04 45.12 41.25 57.89 49.29

Max (ms) (%) 185.37 185.44 262.46 262.63 252.44 260.24 132.18 132.81 261.59 260.08 187.99 186.31

300

250 VM Cost($) Microgrid Cost($) Data Transfer Cost($) TotalCost($)

Cost($)

200

150

100

50

0 Fog1 Fog3 Fog2 Fog5 Fog4 Fog7 Fog6 Fog8 Fog9 Fog10 Fog11

Fogs

Fig. 3. Cost Table 6. Over All Response Time Summary Service Broker policy Closest Data Center Proximity Policy

Average (ms) 66.47

Min (ms)

Max (ms)

37.28

265.80

63.43

23.89

212.66


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4.5 Comparison Results Table 7 demonstrate a comparison of SLF with RR on the basis of response time. The average , min , max response time of each policy is compared. Table 7. Over All Response Time Summary Load Balancing Techniques Round Robin Shortest Load First

Average (ms)

Min (ms)

Max (ms)

66.47 53.57

37.28 35.54

256.80 73.94

5 Conclusion The SG has integrated with cloud and fog based environment for effective sharing of resources among the consumers of a residential area. The purpose of this research is to effectively manage the energy consumption. For this purpose, energy management as a service over fog. To manage the load on fog we have use SLF load balancing algorithm along with two service broker policies: service proximity and optimize response time. The resultS of proposed schemes are slightly better than the existing one. However, the objective function to minimize the cost is efficiently achieved with SLF. So we suggest that in future SLF needs to combine with RR so outperform in the response time as well.

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