Dynamic Optimization Solution for Green Service. Migration in Data Centres. Raymond Carroll, Sasitharan Balasubramaniam, Dmitri Botvich, William Donnelly.
This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
Dynamic Optimization Solution for Green Service Migration in Data Centres Raymond Carroll, Sasitharan Balasubramaniam, Dmitri Botvich, William Donnelly TSSG, Waterford Institiute of Technology, Ireland {rcarroll, sasib, dbotvich, wdonnelly}@tssg.org Abstract— While many aspects of the Future Internet are uncertain, one thing that is clear is that service demand will continue to rise. Also, advances in mobile devices and service technology will almost certainly cause service usage patterns to vary considerably. Another issue that the Future Internet community must be acutely aware of is the huge movement towards more sustainable forms of computing and communications technology. With the recent attention that has been put on IT energy consumption (data-centres in particular), all computing and communications systems need to consider their environmental impact from the outset. With that in mind, we propose a solution for determining the optimal placement of services in data-centre network, in order to maximize the overall renewable energy usage and minimize the cooling energy consumption. We then perform a series of experiments in order to evaluate our solution, incorporating dynamic service request profiles and actual weather and renewable energy production values.
growth in both the number and size of data-centres. These data-centres, with highly power-dense racks, require huge amounts of energy, not just for processing but for cooling also. Recent EPA [1] reports have highlighted this fact, outlining the growing energy burden data-centres are placing on the world’s energy. In light of dwindling fossil fuels, most developed countries have undertaken significant sustainable energy initiatives, such as large scale wind and solar production facilities. The principal issue with many renewable energy sources is that the production levels can vary greatly based on weather conditions. Also there is a great variance in level of production from country to country depending on each countries energy policy. This can be seen in Figure 1, where average values for the Renewable Energy to Total Energy Production Ratio (RER), as well as their deviation, is shown for a number of countries over the course of 24 months.
Index Terms— Green Data Centres, Energy Efficiency, Genetic Algorithm
I.
INTRODUCTION
As the communications research community looks towards the Future Internet, there are a number of emerging trends that need to be considered. Increasing modes of network access, specifically through smart-mobile devices, are set to grow Internet and service usage even further than current levels. This will also lead to new service-usage patterns, as users access services in ways not possible before. The growth in services themselves is also significant. Video and multimedia services (e.g. IPTV, Youtube), as well as P2P and social networking have exploded in popularity, placing a significant additional burden on the communications infrastructure. In the future, as the demand for new services increases so too will the number and variety of services available. There is also considerable research ongoing in service-based architectures, where services can compose to dynamically form applications. These new service behaviours again lead to altered service usage patterns and increased levels of traffic. In essence, it is our view that service traffic across the Internet is going to grow and diversify, in terms of both the types of traffic and its behavioral patterns. As a result the traffic patterns for future Internet services will be much more dynamic, and this needs to be addressed in any Future Internet solution. Meanwhile, the drive towards more environmentally sustainable computing has escalated exponentially in the last number of years. The increased popularity of the Internet, as well as developments like cloud computing, have led to a huge
Figure 1 – Average Renewable Energy Ratios & Deviation
Given the expected levels of variance in Future Internet service traffic, along with the variance of renewable energy production and weather patterns across different countries, the energy sustainability of a given data centre can vary greatly over time. In this paper we propose a mechanism to exploit this variance in order to maximise the quantity of renewable energy consumed by data-centres and to minimize their energy consumption. In order to do this we applied a Genetic Algorithm (GA) to determine the optimal location of services given the data-centres current renewable and cooling energy data, the services in execution and their request levels. We also investigate the effect of varying traffic on the operation of the algorithm. Some research has looked at similar areas and approaches. Many have focused on consolidating workloads on a minimum number of servers in order to allow servers to be switched off/sleep to save power [2][3][4][5]. In [6] biological mechanisms were used to determine more efficient servers in a
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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings
data centre where load is subsequently moved. Others investigated how to make the data centres more efficient by reducing the load on the cooling systems through better workload scheduling within and between data centres [7][8][9][10][11]. In [12] a similar approach to ours is taken, where HPC applications are scheduled based primarily on the data-centres carbon emission rate, but do not employ a GAbased approach. By using the carbon emissions as a key metric there is no guarantee that the resulting load increases will consume clean energy. In our work we go to the root of the energy issue by migrating based on renewable energy quantity. Also, in [13] traffic load is moved between data centres based on electricity costs. Here sustainability of the data centres is not considered at all, meaning possible energy savings would be sacrificed in favour of cost savings. In Section II we describe the general GA-based solution, and in Section III explain the genetic algorithm itself and its mode of execution. Section IV describes the setup of our simulation, while Section V discusses our results. Finally in Section VI we draw our conclusions. II.
GA-BASED SERVICE MIGRATION SOLUTION
In this section we outline our GA-based solution for renewable-energy aware service migration. Our approach is based on the premise that data-centres are often located in different countries and/or continents, and so at any given time may have different environmental conditions, traffic patterns and sources of energy (Figure 2). Therefore, depending on these conditions, both the renewable and cooling energy usage of the data-centres can vary significantly at different times. As described before, our aim is to move services between datacentres by finding the optimal location in terms of renewable energy consumption, cooling costs and service usage.
In the second phase, once the GADC has all the relevant information, it attempts to determine if the GA is required to execute. This is done by examining the current request rates and energy information in order to determine if there is significant fluctuation to warrant GA execution. If there is little fluctuation in these values then a re-configuration of the services is futile, however when the fluctuations are large the energy savings may warrant the re-configuration. This dynamic GA triggering is dealt with in sub-section III.A. Once the GA runs it optimizes the service placement based on the request rate and energy values at that time period. When finished the GA returns the new service configuration, which is then diffused to all data centres. Finally, in phase 3, each data centre takes the new service configuration and compares it to its own configuration. Services that have been elected to move to a new DC are then migrated by the DC. III.
GENETIC ALGORITHM
In our genetic algorithm the fitness function consists of two parts, namely the renewable energy consumed and the cooling energy consumed. These are essentially conflicting optimization goals, since the data centres with the best renewable energy ratio are not necessarily the data centres with the best environmental conditions for efficient cooling. It might be argued that the complexity of this problem may not warrant a GA solution. However in the future we aim to add additional optimisation goals (such as minimizing migration distance to maintain QoS), so we use GA as a framework for modelling our problem in a way that can be later extended to more complex scenarios. Let the set of services be Si = {s1, s2,...si...sN}, where N is the total number of services; the set of Data Centres be DCj={DC1, DC2,....DCj.....DCM}, where M is the total number of Data Centres. Let RERj be the Renewable Energy Ratio of data centre j, CEj be the Cooling Energy of Data Centre j, slij be the service load of service i on data centre j, and DCCj is the capacity of data centre j. In Eq. 1 below we present our fitness function which attempts to maximise the renewable energy consumed and minimise the cooling energy used. j
