Cooperative offloading in mobile edge computing enables resource-constrained edge
clouds to help each other with computation-intensive tasks. However, the power of such …

A big parallel processing job can be delayed substantially as long as one of its many tasks is
being assigned to an unreliable or congested machine. To tackle this so-called straggler …

Computational sprinting speeds up query execution by increasing power usage for short
bursts. Sprinting policy decides when and how long to sprint. Poor policies inflate response …

Mobile Edge Computing (MEC) is a promising computing paradigm that provides cloud
computing services in proximity to end users. Due to the bursty and spatially imbalanced …

Workload scaling is an approach to accelerating computation and thus improving response
times by replicating the exact same request multiple times and processing it in parallel on …

Spark is a big data processing framework based on MapReduce, whose calculation model
requires that all tasks in all parent stages are completed before starting a new stage …

Replicating redundant requests has been shown to be an effective mechanism to defend
application performance from high capacity variability-the common pitfall in the cloud. While …

Wimpy virtual instances equipped with small numbers of cores and RAM are popular public
and private cloud offerings because of their low cost for hosting applications. The challenge …

Today's big data clusters based on the MapReduce paradigm are capable of executing
analysis jobs with multiple priorities, providing differential latency guarantees. Traces from …

Offering consistent low latency remains a key challenge for distributed applications,
especially when deployed on the cloud where virtual machines (VMs) suffer from capacity …