Virtual Machine Placement Optimization for Big Data Applications in Cloud Computing

Big data and cloud computing are two advanced technologies that have overcome many analytical challenges in recent years. With the rise applications of these technologies, necessity efficiency optimization utilization related resources has made sense. The procedure locating virtual machines ( VM ) physical xmlns:xlink="http://www.w3.org/1999/xlink">PM affects performance, speed, costs services. placement is an NP-hard problem. Indeed, problem more complicated big tasks due to need for transferring high volumes traffic between s. This paper proposes a new approach multi center (DC) environment. aware genetic algorithm first fit xmlns:xlink="http://www.w3.org/1999/xlink">AGAFF context-aware distinguishes with input tag uses structure minimize MapReduce nodes. multi-objective based on algorithm, which incorporated methodology. minimizes energy usage by minimizing number used servers, intra-DC tasks, s’ live migration while maximizing relevant CPU RAM every server. Furthermore, it improves job execution time, especially processing, reduces service level agreement (SLA) violations. A comparison results four other algorithms shows about 61% consumption reduction average different scales approves decrease needed s, migrations.