Implications of Workload Management in Hybrid and Multi-Cloud

With the evolving scenario in Cloud computing everchanging, there are numerous cloud service providers in the market offering variety of services and newer capabilities. The resultant is the impending growth in hybrid / multi-cloud architectures, fueled typical choices of organizations opting for the best of breeds in the cloud to meet their overall needs.
This is also bringing out the inevitable challenges of multiple ‘cloud silos’, faced by these enterprises while adopting the hybrid/ multi-cloud infrastructures and reaping its benefits. Here we discuss few typical challenges faced by organizations while managing workloads across ‘cloud silos’ in typical hybrid / multi-cloud deployments.
Cloud native lock-in for workload provisioning and migration
There are very specific interfaces to mange and provision of workloads with the cloud service providers, leading to tightly coupled workload provisioning engines.This creates challenges to seamlessly provision and migrate workloads across multiple clouds and embrace diverse cloud providers.
An efficient approach to this challenge is to isolate the workload provisioning and migrations from the cloud-native workload management interfaces and make it void for the fundamentals of cloud.
Fragmented outlook to resources across clouds
The storage resources and compute of a particular cloud is usually leveraged and viewed independently of the resource pools in other clouds.This eventually leads to scaling, disconnected scheduling and monitoring of workloads across clouds, which also creates significant operational overhead.
An optional way of handling this to create a single viewpoint approach into your resource pools, in order to optimally manage the workloads across the basement, public and the private clouds.This approach offers a general view of the available and utilized resources which will generate the ideal workload scheduling and scaling.
Utilization of Cloud resources         
Workloads are sometimes strategically deployed on pre-determined hosts and VMs in the cloud which creates static compute and storage partitions. This leads to sprawling of the finest VMs and hosts.These static partitions create barriers to run mixed workloads which leads to sub-optimal usage of resources.
One solution for this can be is to create a shared resource pool across multiple clouds and dynamically allocate resources to the respective workloads based on policies and changing needs. This will enable optimal execution of mixed workloads and eliminate static partitioning.
Inability to automatic assigning across clouds
Workloads related to cloud are usually very resilient across the cloud, which automatically recovers in case of a failure.However, there might be some complications with the workload when a particular cloud is inaccessible due to site disasters, planned and unplanned cloud outages.
A recommended way to tackle this glitch is to setup a distributed system which can detect failures/outages in a particular cloud, and then automatically failover the workload to a different cloud.
Obstruction in development and performance for distributed workloads
Distributed workloads are usually deployed across wide geographies leveraging multiple clouds.These workloads tend to use public internet while communicating and exchanging data between clouds,leading to unpredictable latency causing slow performance.
Using Platform Equinix capabilities you canenable private low-latency connections across cloud so as to improve performance.