Adnan. IEEE CLOUD Energy Efficient Geographical Load Balancing via Dynamic Deferral of Workload Muhammad Abdullah Adnan Ryo Sugihara (Amazon.com) Rajesh K. Gupta Department of CSE University of California San Diego (UCSD)

Adnan. IEEE CLOUD Data Centers: Energy Consumption Energy expenses become increasingly important –61 million MWh per year, costing about 4.5 billion dollars: Growing very fast –Millions of dollars for companies every year Increasing energy prices and rise of cloud computing –Energy efficient Cloud Significant research on improving energy efficiency Data Center

Adnan. IEEE CLOUD Geographical Load Balancing Cloud Computing can be utilized for energy efficient computing. –Increasing energy prices. –ability to dynamically track these price variations. Geographical Load Balancing techniques have been suggested for data centers hosting cloud computation –exploit the electricity price differences across regions.

Adnan. IEEE CLOUD Qureshi et al. [ACM SIGCOMM 2009] Geographical Load Balancing –reducing the electricity cost in a wholesale market environment. –Lower electricity bill by adapting the load balancing with dynamic electricity price variation. Electricity Markets –Day-ahead markets (futures) Hourly price predicted for the following day –Real-time markets (spot) Prices are calculated every five minutes, based on actual conditions, rather than expectations. More volatile – provides opportunities for savings. Our work

Adnan. IEEE CLOUD Buchbinder et al.’s Approach [IFIP Networking 2011] Online algorithms for migrating jobs between data centers, –fundamental tradeoff between energy and bandwidth costs. Sophisticated methods to reduce the computational complexity of the proposed heuristics. Drawbacks –Elementary cost vectors Large number of iterations –Discretization of continuous update rule Computationally costly –Bounded Competitive Ratio constant/fixed workload - √ varying workload – x –No deadline requirement.

Adnan. IEEE CLOUD Liu et al.’s Algorithm [ACM SIGMETRICS 2011] Distributed algorithms for Geographical Load Balancing –Multiple sources for workload. –Incorporated capacity provisioning inside data centers Only homogeneous servers Investigated how renewable energy can be used to lower the electricity price of brown energy. Drawbacks –No bound on the maximum delay. –No workload migration.

Adnan. IEEE CLOUD Dynamic Deferral Cloud Computing and Mobile Computing –More and more computation has been outsourced to the cloud. –Different types of workload Delay sensitive, response time/throughput guarantee, Completion time/deadline requirement. Service level agreement (SLA) –Latency requirement –Often has some flexibility We use the flexibility from different SLAs for geographical load balancing to reduce energy consumption. –Defer some of the workload to execute later when electricity price is low –Utilize the slackness in the execution of jobs for energy savings.

Adnan. IEEE CLOUD Assumptions Temporal and Geographical variation of electricity prices. –Variation is unpredictable. –Migrate jobs between data centers cloud service providers have many replication of their data. We consider data centers as computation units. –Homogeneous/heterogeneous Workloads arrive at a central dispatcher. –Dispatcher cannot store workload –Makes load balancing decision dispatcher CLOUD

Adnan. IEEE CLOUD Geographical Load Balancing x i,d,t z i,j,d,t i j migration assignment

Adnan. IEEE CLOUD Model Formulation Workload Model –Workload L t released at time t has Deadline D t Cost Model –Energy cost Proportional to the workload piecewise linear function –Bandwidth cost Cost of migration x i,d,t z i,j,d,t i j t t+1 …… t+D LtLt

Adnan. IEEE CLOUD Model Formulation Assumption: uniform deadline –Deadline is same for all the jobs The net amount of workload executed at data center i at time t assigned + migrated in - migrated out

Adnan. IEEE CLOUD Offline Formulation Future price known => there exists optimal solution without migration. –Dispatcher can always make the correct assignment. Total migration cannot exceed total assignment Execution costMigration cost Total assignment equals total released workload

Adnan. IEEE CLOUD Online Challenges Unpredictable future electricity cost. –How much to execute at current time? –How much to defer to execute later? –How much to migrate and where? Future workload is also unknown –Online algorithm Decide x t & z t online time0 t

Adnan. IEEE CLOUD Our Approach Decouple migration from Dispatcher – Assignment –based on the current electricity prices and future price DC - Migration Decision –The predicted electricity prices by the dispatcher may contain prediction errors. –Data centers correct that error by migrating jobs between each other at later time slots.

Adnan. IEEE CLOUD Dispatcher – Assignment The dispatcher distributes the workload among n data centers. t t+1 …… t+D LtLt t t+1 t+D DC1 DC2 DCn t t+1 t+D

Adnan. IEEE CLOUD DC Adjust assignment with dynamic electricity price variation. –Moving workload at earlier time slots. –Migrating workload between data centers.

Adnan. IEEE CLOUD Formulation w/o Migration Workload assigned at later time slots can only be moved to previous time slots. t t+1 t+D unexecuted workload Data Center 1 Data Center 2 Data Center n Total execution should be equal execution cannot be less than unexecuted workload

Adnan. IEEE CLOUD Formulation with Migration t t+1 t+D unexecuted workload Data Center 1 Data Center 2 Data Center n Workload can migrate between data centers every data center does some work

Adnan. IEEE CLOUD DC - Migration Decision t t+1 t+D unexecuted workload assigned workload Migrated-in workload Migrated-out workload t t+1 t+D

Adnan. IEEE CLOUD How good is the algorithm? No online algorithm has constant competitive ratio with respect to the offline formulation. Lemma t t+1 …… t+D L t = L t+1 = M Proof CASE 1: x D,t ≠ 0 Adversary Method t t+1 …… t+D Offline t t+1 …… t+D Any Online Competitive Ratio = = K’/K, arbitrary Online Cost Offline Cost t t+1 …… t+D β t = Kβ t+D β t+i = K’β t K’ > K

Adnan. IEEE CLOUD No online algorithm has constant competitive ratio with respect to the offline formulation. How good is the algorithm? Lemma t t+1 …… t+D L t = M Proof CASE 2: x D,t = 0 Adversary Method t t+1 …… t+D Offline t t+1 …… t+D Any Online Competitive Ratio = = K, arbitrary Online Cost Offline Cost t t+1 …… t+D β t = Kβ t+D β t+i = K’β t K’ > K

Adnan. IEEE CLOUD How good is the algorithm? Since the competitive ratio cannot be bounded, we compare the online algorithm with much simpler online algorithms. Suppose Online A lgorithm Prediction E rror M igration AEM√√ AE√x Axx

Adnan. IEEE CLOUD How good is the algorithm? Cost(AEM) ≤ Cost(AE) Lemma Proof t t+1 t+D unexecuted workload Data Center 1 Data Center 2 Data Center n Let Δy = amount of migrated workload y = amount of non- migrated workload Cost AEM (y) = Cost AE (y) Migration happens only when Cost of execution of Δy at earlier time slot + cost of migration of Δy ≤ Cost of execution of Δy at later time slot Cost AEM (Δy) ≤ Cost AE (Δy) Cost(AEM) = Cost AEM (y) + Cost AEM (Δy) ≤ Cost AE (y) + Cost AE (Δy) ≤ Cost(AE)

Adnan. IEEE CLOUD How good is the algorithm? Cost(AEM) ≤ Cost(AE) Lemma Cost(AE) ≤ (1+ε) Cost(A) Lemma + Proof β’ – ε ≤ β ≤ β’ + ε Prediction error, ε Predicted price, β’ Actual price, β Cost(AE) Cost(A) α + β’y α + βy ε β 1 + =≤ ≤ 1 + ε

Adnan. IEEE CLOUD Cost(AEM) ≤ Cost(AE) How good is the algorithm? Lemma Cost(AE) ≤ (1+ε) Cost(A) Lemma + Cost(AEM) ≤ (1+ε) Cost(A) Theorem ‖

Adnan. IEEE CLOUD Electricity Price Prediction We model future prices within 24-hr time-frame with Gaussian random variables with –Means: predicted prices by moving average from current day prices. –Variance: estimated from the history by the weighted average price prediction filter. By using two different methods for mean and variance, we exploit both temporal and historical correlation of electricity prices.

Adnan. IEEE CLOUD Evaluation - Electricity Price Four data centers geographically located at four different locations. five minute locational marginal electricity prices in real time market on 15th February, 2012 for four different regions.

Adnan. IEEE CLOUD Evaluation - Workload Two MapReduce Traces from Facebook –Cluster of 600 machines over 24 hours. –Time slot length of 5 minutes because electricity prices vary with an interval of 5 minutes. Workload AWorkload B

Adnan. IEEE CLOUD Evaluation - Deadline We vary deadline 1-12 slots and compare cost reduction with respect to the greedy algorithm without deferral by Qureshi et al. Dynamic deferral can provide around 30% cost savings for deadlines of 12 slots (1 hour) and even for one slot we can get 5% cost savings. Workload AWorkload B

Adnan. IEEE CLOUD Evaluation - Deadline We compare the total cost from the algorithms AEM and AE. The total cost from AEM is always less than the AE as claimed in Lemma. As deadline increases prediction error increases (AE) but cost decreases (AEM) due to flexibility of migration. Workload A Workload B

Adnan. IEEE CLOUD Non-uniform Deadline Workload decomposed according to their associated deadline, L d,t, 0 ≤ d ≤ D Then we replace the release constraints in the formulations by Deadline assignment by k-means clustering based on sizes (map, shuffle and reduce bytes) 15.64% cost reduction for Workload A 9.23% cost reduction for Workload B

Adnan. IEEE CLOUD Summary of Findings Formulation for geographical load balancing with deferral –Uniform deadline –Non-uniform deadline Characterization of optimal offline solution Online Algorithm –Formulation with migration –Formulation without migration Future work –Heterogeneity in data centers/cloud. –Availability of renewable energy.

Adnan. IEEE CLOUD Thank You ?