Intel Corporation

Ananth S Narayan

Piyush Gupta

Mohan J Kumar

Mrittika Ganguli

Subramony Sesha

Archana Patni

Rahul Khanna

Andrew J Herdrich

Anjaneya Reddy Chagam Reddy

Jaideep Moses

Muthuvel M I

Mariusz Oriol

Lukasz Grzymkowski

In accordance with some embodiments, a cloud service provider may operate a data center in a way that dynamically reallocates resources across nodes within the data center based on both utilization and service level agreements. In other words, the allocation of resources may be adjusted dynamically based on current conditions. The current conditions in the data center may be a function of the nature of all the current workloads. Instead of simply managing the workloads in a way to increase overall execution efficiency, the data center instead may manage the workload to achieve quality of service requirements for particular workloads according to service level agreements.

Managing data center resources to achieve a quality of service

Technologies for contention-aware cloud compute scheduling include a number of compute nodes in a cloud computing cluster and a cloud controller. Each compute node collects performance data indicative of cache contention on the compute node, for example, cache misses per thousand instructions. Each compute node determines a contention score as a function of the performance data and stores the contention score in a cloud state database. In response to a request for a new virtual machine, the cloud controller receives contention scores for the compute nodes and selects a compute node based on the contention score. The cloud controller schedules the new virtual machine on the selected compute node. The contention score may include a contention metric and a contention score level indicative of the contention metric. The contention score level may be determined by comparing the contention metric to a number of thresholds. Other embodiments are described and claimed.

Cloud compute scheduling using a heuristic contention model

Techniques and apparatus for managing a distributed computing environment using event digests are described. In one embodiment, for example, an apparatus may include at least one memory, and logic for a system manager, at least a portion of the logic comprised in hardware coupled to the at least one memory, the logic to determine a workload to schedule, access an event digest associated with a plurality of compute hosts, the event digest comprising event digest values determined using out-of-band information, determine metrics from the event digest, generate at least one host weight for at least a portion of the plurality of compute hosts based on the metrics, identify at least one candidate host from the portion of the plurality of compute hosts based on the at least one host weight, and schedule the workload on the at least one candidate host. Other embodiments are described and claimed.

Techniques for managing a distributed computing environment using event digests

Systems described herein operate to improve network performance in a multi-tenant cloud computing environment. Systems can include communication circuitry and processing circuitry to generate a phase sequence matrix that indicates the identity and number of phases of a workload by measuring resources of the compute node during execution of the workload throughout a lifetime of the workload. The processing circuitry can generate a workload fingerprint that includes the phase sequence matrix and a phrase residency matrix. The phase residency matrix can indicate the fraction of execution time of the workload spent in each phase identified in the phase sequence matrix. A cloud controller can access the workload fingerprint for multiple workloads operating on multiple compute nodes in the cloud cluster to adjust workload allocations based at least on these workload fingerprints and on whether service level objectives (SLOs) are being met.

Performance assurance using workload phase detection

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Bengaluru, India

Ananth S Narayan