E-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing Dzmitry KliazovichUniversity of Luxembourg, Luxembourg.

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Presentation transcript:

e-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing Dzmitry KliazovichUniversity of Luxembourg, Luxembourg Pascal Bouvry Sisay T. ArzoUniversity of Trento Fabrizio Granelli Samee U. KhanNorth Dakota State University, U.S.A.

Cloud Computing Cloud computing market: $241 billion in 2020 Main focus is on Software-as-a-Service (SaaS) Aug 22, 2013Dzmitry Kliazovich Source: Larry Dignan, “Cloud computing market”, ZDNet, 2011.

Cloud Computing Applications Aug 22, 2013Dzmitry Kliazovich

Resource Requirements of Cloud Applications Aug 22, 2013Dzmitry Kliazovich Computing Network Bandwidth Communication delays (tolerance) Degree of interactivity Storage

Resource Requirements of Cloud Applications Aug 22, 2013Dzmitry Kliazovich Computing Network Bandwidth Communication delays (tolerance) Storage Degree of interactivity

Cloud Computing Applications Aug 22, 2013Dzmitry Kliazovich Communication resources

Cloud Computing Applications Traditional resource allocation and scheduling – Distribute incoming jobs to the pool of servers – Communication requirements and networking are not taken into account Aug 22, 2013Dzmitry Kliazovich

Scheduling in Data Centers Aug 22, 2013Dzmitry Kliazovich Network congestion!!!

Scheduling in Data Centers Aug 22, 2013Dzmitry Kliazovich Network is balanced !!!

eSTAB Scheduling

eSTAB Scheduling in Data Centers Aug 22, 2013Dzmitry Kliazovich e-STAB: Energy-Efficient Scheduling for Cloud Computing Applications with Traffic Load Balancing Treat communication and computing demands equally #1 Optimize energy efficiency and load balancing of network traffic #2 Formal model for selection of servers, racks, and network modules #3

eSTAB Scheduling in Data Centers Aug 22, 2013Dzmitry Kliazovich Step 1 – Select servers connected to the data center network with the highest available bandwidth (low network load) Step 2 – Within the selected group of servers, select a computing server with the smallest available computing capacity (high server load)

Step #1: Selecting a Rack

eSTAB Model Aug 22, 2013Dzmitry Kliazovich

eSTAB Model Aug 22, 2013Dzmitry Kliazovich

eSTAB Model Aug 22, 2013Dzmitry Kliazovich

eSTAB Model Aug 22, 2013Dzmitry Kliazovich Available bandwidth weighted with the size of the bottleneck queue Favor Empty Queues Penalize Highly-Loaded Queues

eSTAB Model Aug 22, 2013Dzmitry Kliazovich

eSTAB Model Aug 22, 2013Dzmitry Kliazovich eSTAB traffic related metric

Step #2: Selecting a Server

eSTAB Model Aug 22, 2013Dzmitry Kliazovich

eSTAB Model Aug 22, 2013Dzmitry Kliazovich

eSTAB Model Aug 22, 2013Dzmitry Kliazovich

eSTAB Model Aug 22, 2013Dzmitry Kliazovich eSTAB metric for server selection Penalize Selection of Idle Servers Select Servers According to their Energy Consumption

Performance Evaluation

Cloud Computing Simulator Aug 22, 2013Dzmitry Kliazovich – Measures cloud performance and energy efficiency – First to simulate cloud communications with packet-level precision – Implements network-aware scheduling – Implements complete TCP/IP protocol stack available at available at

Simulation Setup Setup Parameters Aug 22, 2013Dzmitry Kliazovich

e-STAB Results Aug 22, 2013Dzmitry Kliazovich Processing Load Distribution Among Servers Racks are overloaded Racks load is balanced

e-STAB Results Aug 22, 2013Dzmitry Kliazovich Traffic Distribution Among Racks Racks are overloaded Racks load is balanced

e-STAB Results Aug 22, 2013Dzmitry Kliazovich Task Completion Delay 80 ms (Green) 20 ms (e-STAB)

e-STAB Results Aug 22, 2013Dzmitry Kliazovich Energy Consumption Improved Performance Comes at a Price of Increased Energy Consumption of Network Switches

Conclusions Considering communication fabric is essential to allocate resource efficiently in cloud computing e-STAB is a new communication-aware scheduler for cloud application e-STAB minimizes communication-related delays and can avoid congestion-related packet losses at a price of minor increase in energy consumption of network switches Aug 22, 2013Dzmitry Kliazovich

Thank you! Contact information: Dzmitry Kliazovich University of Luxembourg